1. Field of the Invention
The present invention relates to a quantification method and an imaging method, and more particularly, to the quantification method capable of quantifying the margin feature, the cysts feature, the calcifications feature, the echoic feature and the heterogenesis feature of a tumor, and to the imaging method capable of imaging the margin feature, the cysts feature, the calcifications feature and the heterogenesis feature of a tumor.
2. Description of Related Art
In recent years, since the resolution of the image and the digitized process of the image of the medical use ultrasonic imaging technology have both increased significantly, the application filed of the medical use ultrasonic imaging technology is not limited in monitoring the growth of a baby anymore. The medical use ultrasonic imaging technology is gradually used in the diagnosis of the nature of many kinds of tumor, such as the thyroid tumor. Moreover, due to the non invasion imaging nature of the medical use ultrasonic imaging technology, more and more doctors uses the medical use ultrasonic imaging technology as an assistance in diagnosing the nature of a tumor and evaluating the possible treatments to the tumor.
The first step for a doctor to diagnose the nature of a tumor through an ultrasonic image is to identify the contour of the tumor, i.e. the tumor contour, for defining the tumor inner region and the tumor external region in the ultrasonic image. Then, the doctor can identify the features of the tumor, such as the margin feature, the cysts feature, the calcifications feature, the echoic feature and the heterogenesis in the tumor inner region, as the reference for diagnosing the nature of the tumor. However, the medical use ultrasonic imaging system of the present time can only allow the doctor to input the tumor contour identified by his naked eyes to the ultrasonic image of the tumor, with the help of a handwriting input device. But, the unreliability of the diagnosis of the nature of the tumor is significant just due to this tumor contour identifying process, for the reasons below:
The tumor contour identifying process mainly depends on the subjective feeling and experience of the doctor, even the mental condition of the doctor can also plays an important role in it. As a result, for a certain ultrasonic image of a tumor, the tumor contours indentified by different doctors are different from each other, as shown in FIG. 1A. Moreover, even for the same doctor, the tumor contours identified at different time slot are different from each other, either.
After that, with the assistance of the identified tumor contour, the doctor can identify whether the above features of the tumor is existed in the tumor inner region by his naked eyes. And if there exists at least one of the above features of the tumor in the tumor inner region, the ratio of the at least one of the above features of the tumor in the tumor inner region is evaluated by the doctor. At final, based on the results collected on hand, such as the distribution of certain kind of feature of the tumor, the doctor diagnoses the nature of the tumor. In other words, no objective mechanism exists in the present diagnosis process of the nature of a tumor, based on the ultrasonic image. Therefore, mistakes on diagnosis of the nature of a tumor exist from time to time, decreasing the reliability of the diagnosis process of the nature of a tumor by the medical use ultrasonic imaging technology to the medical field and the society.
Besides, lots of image margin identification methods have been proposed in the image identification field (such as the license identification field) for example, the snake algorithms. However, before the snake algorithms starts to work, the uses needs to input a preliminary margin into the algorithms, which means the doctor still needs to input a rough tumor contour through the handwriting input device. Then, the snake algorithms continues to execute the following algorithm process. But, due to the intrinsic property thereof, the snake algorithms is preferably applied in the case of the image having obvious margin, or the result of the snake algorithms will be extremely different from the actual margin. However, in most cases, the margin of a tumor is not obvious, i.e. the margin of the tumor is always blurred. Therefore, even with the snake algorithms, the resulting tumor contour is still quite different from the actual contour of the tumor, ash shown in FIG. 1B.
Besides, for decreasing the calculation time of the snake algorithms, the doctor still needs to spend lot of time to input a preliminary margin close to the actual tumor contour into the algorithms. Thus, the burden of the doctor is not released significantly. Moreover, since the ultrasonic image is a gray scale image, the features of the tumor (such as the margin feature, the cysts feature, the calcifications feature, the echoic feature and the heterogenesis feature) may be displayed as minor change of the gradient value of the gray scale of some of the pixel points of the gray scale image, which is hard to identify by the naked eyes of the doctor. Therefore, the doctor can only identify the existence of these features of a tumor by his feeling, making the diagnosing process of the nature of a tumor merely depending on the subjective feeling of the doctor, not depending on the objective facts.
Therefore, a quantification method capable of quantifying the margin feature, the cysts feature, the calcifications feature, the echoic feature and the heterogenesis feature of a tumor, and an imaging method capable of imaging the margin feature, the cysts feature, the calcifications feature and the heterogenesis feature of a tumor are required in the field.